NORMALIZING MULTI-SUBJECT VARIATION FOR DRIVERS' EMOTION RECOGNITION

The paper attempts the recognition of multiple drivers' emotional state from physiological signals. The major challenge of the research is the severe inter-subject variation such that it is extreme difficult to build a general model for multiple drivers. In this paper, we focus on discovering an optimal feature mapping by utilizing the additional attribute from the drivers. Two models are reported, specifically an auxiliary dimension model and a factorization model. Experimental results show that the proposed method outperform existing algorithms used for emotional state recognition.